A long-running AI task is a distributed-systems problem wearing a chatbot's clothes: duplicate submissions, late events, worker loss, reconnecting clients, and cancellation races all apply. If you self-host, those are your invariants to defend. If you use a hosted platform, you're trusting someone else's worker pool — so you should know what to check.
MonkeyCode can be self-hosted (github.com/chaitin/MonkeyCode, AGPL-3.0), and now offers a hosted version at monkeycode-ai.net, free to start. Either way, the reliability questions are the same; only the owner changes.
Reliability properties to probe
You can black-box test most of these from a client without touching internals:
- Idempotent submission. Submit the same task twice fast. Do you get one task or two?
- Monotonic events. Reconnect mid-task. Do events replay in order and converge, or arrive duplicated/out of order?
- Worker loss recovery. If a task stalls, does the platform surface a clear failed/retry state rather than hanging forever?
- Cancellation semantics. Cancel a running task. Is the terminal state stable and honored, with no zombie work?
What offloading actually buys you
Handing the worker pool to a hosted service removes the operational burden of running it reliably — scheduling, draining, retries, capacity — which is genuinely hard. What it does not remove is your need to verify the guarantees you depend on. Trust, then verify: run the four probes above before you build a workflow that assumes them.
Start at monkeycode-ai.net, free to start, and run the probes yourself — this is a review protocol, not a published benchmark. Before depending on it, ask on the MonkeyCode Discord about current free model-credit availability, eligibility, and limits.
Disclosure: I'm a MonkeyCode user sharing my own experience, not affiliated with the project.
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